Overview

Dataset statistics

Number of variables88
Number of observations9412
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 MiB
Average record size in memory704.0 B

Variable types

Numeric25
Categorical63

Alerts

q5 has a high cardinality: 770 distinct valuesHigh cardinality
id is highly overall correlated with regiaoHigh correlation
q3 is highly overall correlated with q1 and 18 other fieldsHigh correlation
q4 is highly overall correlated with q1 and 18 other fieldsHigh correlation
q6 is highly overall correlated with q1 and 18 other fieldsHigh correlation
q13 is highly overall correlated with q17 and 21 other fieldsHigh correlation
q14 is highly overall correlated with q1 and 20 other fieldsHigh correlation
q16 is highly overall correlated with q1 and 18 other fieldsHigh correlation
q17 is highly overall correlated with q13 and 21 other fieldsHigh correlation
qp1 is highly overall correlated with qp4 and 37 other fieldsHigh correlation
qp4 is highly overall correlated with qp1 and 45 other fieldsHigh correlation
qp7 is highly overall correlated with qp1 and 45 other fieldsHigh correlation
qp10 is highly overall correlated with qp1 and 45 other fieldsHigh correlation
qp13 is highly overall correlated with qp1 and 46 other fieldsHigh correlation
qp16 is highly overall correlated with qp1 and 47 other fieldsHigh correlation
qp19 is highly overall correlated with qp1 and 46 other fieldsHigh correlation
qp22 is highly overall correlated with qp1 and 46 other fieldsHigh correlation
qp25 is highly overall correlated with qp1 and 48 other fieldsHigh correlation
qp28 is highly overall correlated with qp1 and 45 other fieldsHigh correlation
qp31 is highly overall correlated with qp1 and 45 other fieldsHigh correlation
qp34 is highly overall correlated with qp1 and 46 other fieldsHigh correlation
qp37 is highly overall correlated with qp1 and 47 other fieldsHigh correlation
qp43 is highly overall correlated with qp1 and 48 other fieldsHigh correlation
qp46 is highly overall correlated with qp1 and 46 other fieldsHigh correlation
qp49 is highly overall correlated with qp1 and 45 other fieldsHigh correlation
regiao is highly overall correlated with idHigh correlation
q1 is highly overall correlated with q3 and 66 other fieldsHigh correlation
q2 is highly overall correlated with q3 and 42 other fieldsHigh correlation
q7 is highly overall correlated with q3 and 42 other fieldsHigh correlation
q8 is highly overall correlated with q3 and 42 other fieldsHigh correlation
q9 is highly overall correlated with q3 and 42 other fieldsHigh correlation
q10 is highly overall correlated with q3 and 42 other fieldsHigh correlation
q11 is highly overall correlated with q3 and 42 other fieldsHigh correlation
q12 is highly overall correlated with q3 and 42 other fieldsHigh correlation
q15 is highly overall correlated with q3 and 42 other fieldsHigh correlation
q18 is highly overall correlated with q3 and 24 other fieldsHigh correlation
q19 is highly overall correlated with q3 and 24 other fieldsHigh correlation
q20 is highly overall correlated with q3 and 24 other fieldsHigh correlation
q21 is highly overall correlated with q3 and 24 other fieldsHigh correlation
qp2 is highly overall correlated with q13 and 40 other fieldsHigh correlation
qp3 is highly overall correlated with q3 and 42 other fieldsHigh correlation
qp5 is highly overall correlated with qp4High correlation
qp6 is highly overall correlated with qp1 and 36 other fieldsHigh correlation
qp8 is highly overall correlated with qp7 and 1 other fieldsHigh correlation
qp9 is highly overall correlated with qp1 and 33 other fieldsHigh correlation
qp11 is highly overall correlated with qp10 and 1 other fieldsHigh correlation
qp12 is highly overall correlated with qp1 and 31 other fieldsHigh correlation
qp14 is highly overall correlated with qp13 and 4 other fieldsHigh correlation
qp15 is highly overall correlated with qp13 and 14 other fieldsHigh correlation
qp17 is highly overall correlated with qp16 and 5 other fieldsHigh correlation
qp18 is highly overall correlated with qp1 and 36 other fieldsHigh correlation
qp20 is highly overall correlated with qp19 and 6 other fieldsHigh correlation
qp21 is highly overall correlated with qp1 and 35 other fieldsHigh correlation
qp23 is highly overall correlated with qp19 and 3 other fieldsHigh correlation
qp24 is highly overall correlated with qp1 and 36 other fieldsHigh correlation
qp26 is highly overall correlated with qp25 and 2 other fieldsHigh correlation
qp27 is highly overall correlated with qp4 and 31 other fieldsHigh correlation
qp29 is highly overall correlated with qp28 and 2 other fieldsHigh correlation
qp30 is highly overall correlated with qp4 and 31 other fieldsHigh correlation
qp32 is highly overall correlated with qp31 and 1 other fieldsHigh correlation
qp33 is highly overall correlated with qp4 and 32 other fieldsHigh correlation
qp35 is highly overall correlated with qp25 and 3 other fieldsHigh correlation
qp36 is highly overall correlated with qp34 and 14 other fieldsHigh correlation
qp38 is highly overall correlated with qp16 and 4 other fieldsHigh correlation
qp39 is highly overall correlated with qp4 and 31 other fieldsHigh correlation
qp40 is highly overall correlated with q13 and 50 other fieldsHigh correlation
qp41 is highly overall correlated with qp43 and 6 other fieldsHigh correlation
qp42 is highly overall correlated with qp43 and 15 other fieldsHigh correlation
qp44 is highly overall correlated with qp43 and 4 other fieldsHigh correlation
qp45 is highly overall correlated with qp4 and 30 other fieldsHigh correlation
qp47 is highly overall correlated with qp43 and 4 other fieldsHigh correlation
qp48 is highly overall correlated with qp4 and 30 other fieldsHigh correlation
qp50 is highly overall correlated with qp49 and 2 other fieldsHigh correlation
qp51 is highly overall correlated with qp4 and 31 other fieldsHigh correlation
qp52 is highly overall correlated with q3 and 48 other fieldsHigh correlation
qp53 is highly overall correlated with q3 and 43 other fieldsHigh correlation
qp54 is highly overall correlated with q3 and 45 other fieldsHigh correlation
qp55 is highly overall correlated with q3 and 47 other fieldsHigh correlation
r1 is highly overall correlated with q3 and 50 other fieldsHigh correlation
r2 is highly overall correlated with q1 and 8 other fieldsHigh correlation
r3 is highly overall correlated with q1 and 8 other fieldsHigh correlation
r4 is highly overall correlated with q1 and 8 other fieldsHigh correlation
r5 is highly overall correlated with q1 and 8 other fieldsHigh correlation
r6 is highly overall correlated with q1 and 8 other fieldsHigh correlation
r7 is highly overall correlated with q1 and 8 other fieldsHigh correlation
r8 is highly overall correlated with q1 and 8 other fieldsHigh correlation
r9 is highly overall correlated with q1 and 8 other fieldsHigh correlation
q1 is highly imbalanced (78.2%)Imbalance
q8 is highly imbalanced (56.2%)Imbalance
q10 is highly imbalanced (67.9%)Imbalance
q11 is highly imbalanced (75.1%)Imbalance
q12 is highly imbalanced (66.6%)Imbalance
q18 is highly imbalanced (81.9%)Imbalance
q20 is highly imbalanced (61.1%)Imbalance
qp2 is highly imbalanced (88.5%)Imbalance
qp3 is highly imbalanced (85.2%)Imbalance
qp5 is highly imbalanced (98.9%)Imbalance
qp6 is highly imbalanced (92.0%)Imbalance
qp8 is highly imbalanced (98.3%)Imbalance
qp9 is highly imbalanced (93.0%)Imbalance
qp11 is highly imbalanced (98.5%)Imbalance
qp12 is highly imbalanced (92.9%)Imbalance
qp14 is highly imbalanced (98.4%)Imbalance
qp15 is highly imbalanced (94.7%)Imbalance
qp17 is highly imbalanced (99.0%)Imbalance
qp18 is highly imbalanced (92.3%)Imbalance
qp20 is highly imbalanced (98.3%)Imbalance
qp21 is highly imbalanced (92.4%)Imbalance
qp23 is highly imbalanced (98.6%)Imbalance
qp24 is highly imbalanced (92.2%)Imbalance
qp26 is highly imbalanced (98.8%)Imbalance
qp27 is highly imbalanced (93.5%)Imbalance
qp29 is highly imbalanced (98.8%)Imbalance
qp30 is highly imbalanced (92.7%)Imbalance
qp32 is highly imbalanced (98.6%)Imbalance
qp33 is highly imbalanced (92.7%)Imbalance
qp35 is highly imbalanced (98.6%)Imbalance
qp36 is highly imbalanced (94.9%)Imbalance
qp38 is highly imbalanced (98.6%)Imbalance
qp39 is highly imbalanced (93.1%)Imbalance
qp40 is highly imbalanced (89.1%)Imbalance
qp41 is highly imbalanced (99.2%)Imbalance
qp42 is highly imbalanced (94.6%)Imbalance
qp44 is highly imbalanced (99.5%)Imbalance
qp45 is highly imbalanced (92.7%)Imbalance
qp47 is highly imbalanced (98.8%)Imbalance
qp48 is highly imbalanced (93.9%)Imbalance
qp50 is highly imbalanced (98.7%)Imbalance
qp51 is highly imbalanced (93.2%)Imbalance
qp52 is highly imbalanced (88.5%)Imbalance
qp53 is highly imbalanced (88.5%)Imbalance
qp54 is highly imbalanced (85.4%)Imbalance
qp55 is highly imbalanced (88.3%)Imbalance
r1 is highly imbalanced (54.2%)Imbalance
id is uniformly distributedUniform
id has unique valuesUnique
q13 has 184 (2.0%) zerosZeros
q16 has 1760 (18.7%) zerosZeros
q17 has 1504 (16.0%) zerosZeros

Reproduction

Analysis started2023-03-22 18:12:17.502554
Analysis finished2023-03-22 18:15:16.962820
Duration2 minutes and 59.46 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct9412
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4706.5
Minimum1
Maximum9412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:17.079454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile471.55
Q12353.75
median4706.5
Q37059.25
95-th percentile8941.45
Maximum9412
Range9411
Interquartile range (IQR)4705.5

Descriptive statistics

Standard deviation2717.1547
Coefficient of variation (CV)0.5773196
Kurtosis-1.2
Mean4706.5
Median Absolute Deviation (MAD)2353
Skewness0
Sum44297578
Variance7382929.7
MonotonicityNot monotonic
2023-03-22T15:15:17.390379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5831 1
 
< 0.1%
7621 1
 
< 0.1%
7709 1
 
< 0.1%
7773 1
 
< 0.1%
7775 1
 
< 0.1%
7642 1
 
< 0.1%
7714 1
 
< 0.1%
7812 1
 
< 0.1%
7701 1
 
< 0.1%
7749 1
 
< 0.1%
Other values (9402) 9402
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
9412 1
< 0.1%
9411 1
< 0.1%
9410 1
< 0.1%
9409 1
< 0.1%
9408 1
< 0.1%
9407 1
< 0.1%
9406 1
< 0.1%
9405 1
< 0.1%
9404 1
< 0.1%
9403 1
< 0.1%

regiao
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
3
3922 
2
2549 
4
1278 
5
920 
1
743 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 3922
41.7%
2 2549
27.1%
4 1278
 
13.6%
5 920
 
9.8%
1 743
 
7.9%

Length

2023-03-22T15:15:17.715512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:18.048571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3 3922
41.7%
2 2549
27.1%
4 1278
 
13.6%
5 920
 
9.8%
1 743
 
7.9%

Most occurring characters

ValueCountFrequency (%)
3 3922
41.7%
2 2549
27.1%
4 1278
 
13.6%
5 920
 
9.8%
1 743
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 3922
41.7%
2 2549
27.1%
4 1278
 
13.6%
5 920
 
9.8%
1 743
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 3922
41.7%
2 2549
27.1%
4 1278
 
13.6%
5 920
 
9.8%
1 743
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 3922
41.7%
2 2549
27.1%
4 1278
 
13.6%
5 920
 
9.8%
1 743
 
7.9%

idade
Real number (ℝ)

Distinct52
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.558436
Minimum50
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:18.403618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile51
Q155
median62
Q370
95-th percentile83
Maximum105
Range55
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.148388
Coefficient of variation (CV)0.1596702
Kurtosis-0.18425888
Mean63.558436
Median Absolute Deviation (MAD)8
Skewness0.69954335
Sum598212
Variance102.98978
MonotonicityNot monotonic
2023-03-22T15:15:18.763197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 480
 
5.1%
54 459
 
4.9%
52 443
 
4.7%
51 413
 
4.4%
50 402
 
4.3%
59 367
 
3.9%
55 363
 
3.9%
58 358
 
3.8%
57 356
 
3.8%
56 339
 
3.6%
Other values (42) 5432
57.7%
ValueCountFrequency (%)
50 402
4.3%
51 413
4.4%
52 443
4.7%
53 480
5.1%
54 459
4.9%
55 363
3.9%
56 339
3.6%
57 356
3.8%
58 358
3.8%
59 367
3.9%
ValueCountFrequency (%)
105 3
 
< 0.1%
104 1
 
< 0.1%
99 1
 
< 0.1%
98 3
 
< 0.1%
97 5
 
0.1%
96 10
0.1%
95 5
 
0.1%
94 7
0.1%
93 9
0.1%
92 13
0.1%

q1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
1
9084 
0
 
328

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9084
96.5%
0 328
 
3.5%

Length

2023-03-22T15:15:19.075623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:19.343874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 9084
96.5%
0 328
 
3.5%

Most occurring characters

ValueCountFrequency (%)
1 9084
96.5%
0 328
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9084
96.5%
0 328
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9084
96.5%
0 328
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9084
96.5%
0 328
 
3.5%

q2
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
1
6619 
0
2453 
8
 
328
9
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 6619
70.3%
0 2453
 
26.1%
8 328
 
3.5%
9 12
 
0.1%

Length

2023-03-22T15:15:19.578198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:19.863035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 6619
70.3%
0 2453
 
26.1%
8 328
 
3.5%
9 12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 6619
70.3%
0 2453
 
26.1%
8 328
 
3.5%
9 12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6619
70.3%
0 2453
 
26.1%
8 328
 
3.5%
9 12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6619
70.3%
0 2453
 
26.1%
8 328
 
3.5%
9 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6619
70.3%
0 2453
 
26.1%
8 328
 
3.5%
9 12
 
0.1%

q3
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.551105
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:20.112990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2321801
Coefficient of variation (CV)0.34698499
Kurtosis5.0779529
Mean3.551105
Median Absolute Deviation (MAD)1
Skewness1.4615456
Sum33423
Variance1.5182678
MonotonicityNot monotonic
2023-03-22T15:15:20.261726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 4202
44.6%
4 3342
35.5%
5 677
 
7.2%
1 426
 
4.5%
2 418
 
4.4%
8 328
 
3.5%
9 12
 
0.1%
10 7
 
0.1%
ValueCountFrequency (%)
1 426
 
4.5%
2 418
 
4.4%
3 4202
44.6%
4 3342
35.5%
5 677
 
7.2%
8 328
 
3.5%
9 12
 
0.1%
10 7
 
0.1%
ValueCountFrequency (%)
10 7
 
0.1%
9 12
 
0.1%
8 328
 
3.5%
5 677
 
7.2%
4 3342
35.5%
3 4202
44.6%
2 418
 
4.4%
1 426
 
4.5%

q4
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5266681
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:20.373450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3330002
Coefficient of variation (CV)0.52757233
Kurtosis11.510383
Mean2.5266681
Median Absolute Deviation (MAD)0
Skewness3.1137218
Sum23781
Variance1.7768894
MonotonicityNot monotonic
2023-03-22T15:15:20.470579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 4724
50.2%
3 3425
36.4%
1 875
 
9.3%
8 328
 
3.5%
9 41
 
0.4%
10 19
 
0.2%
ValueCountFrequency (%)
1 875
 
9.3%
2 4724
50.2%
3 3425
36.4%
8 328
 
3.5%
9 41
 
0.4%
10 19
 
0.2%
ValueCountFrequency (%)
10 19
 
0.2%
9 41
 
0.4%
8 328
 
3.5%
3 3425
36.4%
2 4724
50.2%
1 875
 
9.3%

q5
Categorical

Distinct770
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
10jan1960
 
699
09jan1960
 
328
09dec2015
 
47
10dec2015
 
43
03dec2015
 
41
Other values (765)
8254 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters84708
Distinct characters29
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique248 ?
Unique (%)2.6%

Sample

1st row12may2015
2nd row12may2015
3rd row13may2015
4th row14may2015
5th row10jan1960

Common Values

ValueCountFrequency (%)
10jan1960 699
 
7.4%
09jan1960 328
 
3.5%
09dec2015 47
 
0.5%
10dec2015 43
 
0.5%
03dec2015 41
 
0.4%
16sep2015 41
 
0.4%
01feb2016 41
 
0.4%
16nov2015 39
 
0.4%
11sep2015 39
 
0.4%
30nov2015 37
 
0.4%
Other values (760) 8057
85.6%

Length

2023-03-22T15:15:20.579969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10jan1960 699
 
7.4%
09jan1960 328
 
3.5%
09dec2015 47
 
0.5%
10dec2015 43
 
0.5%
03dec2015 41
 
0.4%
16sep2015 41
 
0.4%
01feb2016 41
 
0.4%
16nov2015 39
 
0.4%
11sep2015 39
 
0.4%
30nov2015 37
 
0.4%
Other values (760) 8057
85.6%

Most occurring characters

ValueCountFrequency (%)
1 13979
16.5%
0 13641
16.1%
2 11807
13.9%
6 5957
 
7.0%
5 5062
 
6.0%
a 4105
 
4.8%
j 3090
 
3.6%
n 2982
 
3.5%
u 2582
 
3.0%
9 2335
 
2.8%
Other values (19) 19168
22.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 56472
66.7%
Lowercase Letter 28236
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4105
14.5%
j 3090
10.9%
n 2982
10.6%
u 2582
 
9.1%
e 2233
 
7.9%
p 1500
 
5.3%
o 1478
 
5.2%
c 1347
 
4.8%
r 1144
 
4.1%
m 1142
 
4.0%
Other values (9) 6633
23.5%
Decimal Number
ValueCountFrequency (%)
1 13979
24.8%
0 13641
24.2%
2 11807
20.9%
6 5957
10.5%
5 5062
 
9.0%
9 2335
 
4.1%
3 1236
 
2.2%
4 857
 
1.5%
8 811
 
1.4%
7 787
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 56472
66.7%
Latin 28236
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4105
14.5%
j 3090
10.9%
n 2982
10.6%
u 2582
 
9.1%
e 2233
 
7.9%
p 1500
 
5.3%
o 1478
 
5.2%
c 1347
 
4.8%
r 1144
 
4.1%
m 1142
 
4.0%
Other values (9) 6633
23.5%
Common
ValueCountFrequency (%)
1 13979
24.8%
0 13641
24.2%
2 11807
20.9%
6 5957
10.5%
5 5062
 
9.0%
9 2335
 
4.1%
3 1236
 
2.2%
4 857
 
1.5%
8 811
 
1.4%
7 787
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84708
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13979
16.5%
0 13641
16.1%
2 11807
13.9%
6 5957
 
7.0%
5 5062
 
6.0%
a 4105
 
4.8%
j 3090
 
3.6%
n 2982
 
3.5%
u 2582
 
3.0%
9 2335
 
2.8%
Other values (19) 19168
22.6%

q6
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4974501
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:20.689281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q36
95-th percentile7
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8454509
Coefficient of variation (CV)0.41033271
Kurtosis-0.73567896
Mean4.4974501
Median Absolute Deviation (MAD)1
Skewness0.1690714
Sum42330
Variance3.405689
MonotonicityNot monotonic
2023-03-22T15:15:20.809365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 1681
17.9%
5 1634
17.4%
3 1524
16.2%
6 1487
15.8%
2 1348
14.3%
7 1046
11.1%
8 328
 
3.5%
1 248
 
2.6%
9 108
 
1.1%
10 8
 
0.1%
ValueCountFrequency (%)
1 248
 
2.6%
2 1348
14.3%
3 1524
16.2%
4 1681
17.9%
5 1634
17.4%
6 1487
15.8%
7 1046
11.1%
8 328
 
3.5%
9 108
 
1.1%
10 8
 
0.1%
ValueCountFrequency (%)
10 8
 
0.1%
9 108
 
1.1%
8 328
 
3.5%
7 1046
11.1%
6 1487
15.8%
5 1634
17.4%
4 1681
17.9%
3 1524
16.2%
2 1348
14.3%
1 248
 
2.6%

q7
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
1
6950 
0
2134 
8
 
328

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 6950
73.8%
0 2134
 
22.7%
8 328
 
3.5%

Length

2023-03-22T15:15:20.936476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:21.050956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 6950
73.8%
0 2134
 
22.7%
8 328
 
3.5%

Most occurring characters

ValueCountFrequency (%)
1 6950
73.8%
0 2134
 
22.7%
8 328
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6950
73.8%
0 2134
 
22.7%
8 328
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6950
73.8%
0 2134
 
22.7%
8 328
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6950
73.8%
0 2134
 
22.7%
8 328
 
3.5%

q8
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
1
8105 
0
979 
8
 
328

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 8105
86.1%
0 979
 
10.4%
8 328
 
3.5%

Length

2023-03-22T15:15:21.160306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:21.285276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 8105
86.1%
0 979
 
10.4%
8 328
 
3.5%

Most occurring characters

ValueCountFrequency (%)
1 8105
86.1%
0 979
 
10.4%
8 328
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8105
86.1%
0 979
 
10.4%
8 328
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8105
86.1%
0 979
 
10.4%
8 328
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8105
86.1%
0 979
 
10.4%
8 328
 
3.5%

q9
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
1
7723 
0
1361 
8
 
328

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 7723
82.1%
0 1361
 
14.5%
8 328
 
3.5%

Length

2023-03-22T15:15:21.388881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:21.519427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 7723
82.1%
0 1361
 
14.5%
8 328
 
3.5%

Most occurring characters

ValueCountFrequency (%)
1 7723
82.1%
0 1361
 
14.5%
8 328
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7723
82.1%
0 1361
 
14.5%
8 328
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7723
82.1%
0 1361
 
14.5%
8 328
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7723
82.1%
0 1361
 
14.5%
8 328
 
3.5%

q10
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
1
8599 
0
 
485
8
 
328

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8599
91.4%
0 485
 
5.2%
8 328
 
3.5%

Length

2023-03-22T15:15:21.617866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:21.727216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 8599
91.4%
0 485
 
5.2%
8 328
 
3.5%

Most occurring characters

ValueCountFrequency (%)
1 8599
91.4%
0 485
 
5.2%
8 328
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8599
91.4%
0 485
 
5.2%
8 328
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8599
91.4%
0 485
 
5.2%
8 328
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8599
91.4%
0 485
 
5.2%
8 328
 
3.5%

q11
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
0
8701 
8
 
328
2
 
254
1
 
129

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8701
92.4%
8 328
 
3.5%
2 254
 
2.7%
1 129
 
1.4%

Length

2023-03-22T15:15:21.842189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:21.970214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 8701
92.4%
8 328
 
3.5%
2 254
 
2.7%
1 129
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 8701
92.4%
8 328
 
3.5%
2 254
 
2.7%
1 129
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8701
92.4%
8 328
 
3.5%
2 254
 
2.7%
1 129
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8701
92.4%
8 328
 
3.5%
2 254
 
2.7%
1 129
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8701
92.4%
8 328
 
3.5%
2 254
 
2.7%
1 129
 
1.4%

q12
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
1
8552 
0
 
532
8
 
328

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8552
90.9%
0 532
 
5.7%
8 328
 
3.5%

Length

2023-03-22T15:15:22.063931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:22.190571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 8552
90.9%
0 532
 
5.7%
8 328
 
3.5%

Most occurring characters

ValueCountFrequency (%)
1 8552
90.9%
0 532
 
5.7%
8 328
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8552
90.9%
0 532
 
5.7%
8 328
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8552
90.9%
0 532
 
5.7%
8 328
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8552
90.9%
0 532
 
5.7%
8 328
 
3.5%

q13
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1488525
Minimum0
Maximum88
Zeros184
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:22.284304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile8
Maximum88
Range88
Interquartile range (IQR)2

Descriptive statistics

Standard deviation15.450293
Coefficient of variation (CV)2.161227
Kurtosis23.155617
Mean7.1488525
Median Absolute Deviation (MAD)1
Skewness4.9825646
Sum67285
Variance238.71156
MonotonicityNot monotonic
2023-03-22T15:15:22.643451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4 2215
23.5%
5 2027
21.5%
3 1608
17.1%
6 1284
13.6%
2 787
 
8.4%
7 488
 
5.2%
88 328
 
3.5%
1 295
 
3.1%
0 184
 
2.0%
8 160
 
1.7%
Other values (2) 36
 
0.4%
ValueCountFrequency (%)
0 184
 
2.0%
1 295
 
3.1%
2 787
 
8.4%
3 1608
17.1%
4 2215
23.5%
5 2027
21.5%
6 1284
13.6%
7 488
 
5.2%
8 160
 
1.7%
9 27
 
0.3%
ValueCountFrequency (%)
88 328
 
3.5%
10 9
 
0.1%
9 27
 
0.3%
8 160
 
1.7%
7 488
 
5.2%
6 1284
13.6%
5 2027
21.5%
4 2215
23.5%
3 1608
17.1%
2 787
 
8.4%

q14
Real number (ℝ)

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.966001
Minimum0
Maximum999
Zeros10
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:22.986727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q19
median12
Q315
95-th percentile23
Maximum999
Range999
Interquartile range (IQR)6

Descriptive statistics

Standard deviation165.20958
Coefficient of variation (CV)3.7576668
Kurtosis22.539791
Mean43.966001
Median Absolute Deviation (MAD)3
Skewness4.9489214
Sum413808
Variance27294.205
MonotonicityNot monotonic
2023-03-22T15:15:23.333041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
10 961
 
10.2%
11 919
 
9.8%
12 831
 
8.8%
9 780
 
8.3%
13 774
 
8.2%
14 642
 
6.8%
8 618
 
6.6%
15 526
 
5.6%
7 466
 
5.0%
16 414
 
4.4%
Other values (28) 2481
26.4%
ValueCountFrequency (%)
0 10
 
0.1%
1 12
 
0.1%
2 35
 
0.4%
3 59
 
0.6%
4 141
 
1.5%
5 213
 
2.3%
6 379
4.0%
7 466
5.0%
8 618
6.6%
9 780
8.3%
ValueCountFrequency (%)
999 15
 
0.2%
888 328
3.5%
38 1
 
< 0.1%
35 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
31 2
 
< 0.1%
30 6
 
0.1%
29 3
 
< 0.1%
28 7
 
0.1%

q15
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
1
6107 
0
2977 
8
 
328

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 6107
64.9%
0 2977
31.6%
8 328
 
3.5%

Length

2023-03-22T15:15:23.643846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:23.909425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 6107
64.9%
0 2977
31.6%
8 328
 
3.5%

Most occurring characters

ValueCountFrequency (%)
1 6107
64.9%
0 2977
31.6%
8 328
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6107
64.9%
0 2977
31.6%
8 328
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6107
64.9%
0 2977
31.6%
8 328
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6107
64.9%
0 2977
31.6%
8 328
 
3.5%

q16
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7558436
Minimum0
Maximum8
Zeros1760
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:24.135322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7400667
Coefficient of variation (CV)0.99101464
Kurtosis3.3612861
Mean1.7558436
Median Absolute Deviation (MAD)1
Skewness1.6901826
Sum16526
Variance3.0278322
MonotonicityNot monotonic
2023-03-22T15:15:24.290019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 4351
46.2%
0 1760
18.7%
3 1353
 
14.4%
4 1126
 
12.0%
2 494
 
5.2%
8 328
 
3.5%
ValueCountFrequency (%)
0 1760
18.7%
1 4351
46.2%
2 494
 
5.2%
3 1353
 
14.4%
4 1126
 
12.0%
8 328
 
3.5%
ValueCountFrequency (%)
8 328
 
3.5%
4 1126
 
12.0%
3 1353
 
14.4%
2 494
 
5.2%
1 4351
46.2%
0 1760
18.7%

q17
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7690183
Minimum0
Maximum88
Zeros1504
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:24.414989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile7
Maximum88
Range88
Interquartile range (IQR)2

Descriptive statistics

Standard deviation15.734629
Coefficient of variation (CV)2.7274362
Kurtosis23.018873
Mean5.7690183
Median Absolute Deviation (MAD)1
Skewness4.9619281
Sum54298
Variance247.57856
MonotonicityNot monotonic
2023-03-22T15:15:24.524386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 1955
20.8%
4 1579
16.8%
2 1522
16.2%
0 1504
16.0%
5 1028
10.9%
1 840
8.9%
6 455
 
4.8%
88 328
 
3.5%
7 140
 
1.5%
8 39
 
0.4%
Other values (2) 22
 
0.2%
ValueCountFrequency (%)
0 1504
16.0%
1 840
8.9%
2 1522
16.2%
3 1955
20.8%
4 1579
16.8%
5 1028
10.9%
6 455
 
4.8%
7 140
 
1.5%
8 39
 
0.4%
9 13
 
0.1%
ValueCountFrequency (%)
88 328
 
3.5%
10 9
 
0.1%
9 13
 
0.1%
8 39
 
0.4%
7 140
 
1.5%
6 455
 
4.8%
5 1028
10.9%
4 1579
16.8%
3 1955
20.8%
2 1522
16.2%

q18
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
1
8830 
8
 
328
2
 
194
9
 
52
10
 
8

Length

Max length2
Median length1
Mean length1.00085
Min length1

Characters and Unicode

Total characters9420
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row9

Common Values

ValueCountFrequency (%)
1 8830
93.8%
8 328
 
3.5%
2 194
 
2.1%
9 52
 
0.6%
10 8
 
0.1%

Length

2023-03-22T15:15:24.649416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:24.793470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 8830
93.8%
8 328
 
3.5%
2 194
 
2.1%
9 52
 
0.6%
10 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 8838
93.8%
8 328
 
3.5%
2 194
 
2.1%
9 52
 
0.6%
0 8
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9420
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8838
93.8%
8 328
 
3.5%
2 194
 
2.1%
9 52
 
0.6%
0 8
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9420
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8838
93.8%
8 328
 
3.5%
2 194
 
2.1%
9 52
 
0.6%
0 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9420
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8838
93.8%
8 328
 
3.5%
2 194
 
2.1%
9 52
 
0.6%
0 8
 
0.1%

q19
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
1
4599 
2
3989 
9
 
453
8
 
328
10
 
43

Length

Max length2
Median length1
Mean length1.0045686
Min length1

Characters and Unicode

Total characters9455
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 4599
48.9%
2 3989
42.4%
9 453
 
4.8%
8 328
 
3.5%
10 43
 
0.5%

Length

2023-03-22T15:15:24.910576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:25.051236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 4599
48.9%
2 3989
42.4%
9 453
 
4.8%
8 328
 
3.5%
10 43
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1 4642
49.1%
2 3989
42.2%
9 453
 
4.8%
8 328
 
3.5%
0 43
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9455
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4642
49.1%
2 3989
42.2%
9 453
 
4.8%
8 328
 
3.5%
0 43
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9455
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4642
49.1%
2 3989
42.2%
9 453
 
4.8%
8 328
 
3.5%
0 43
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9455
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4642
49.1%
2 3989
42.2%
9 453
 
4.8%
8 328
 
3.5%
0 43
 
0.5%

q20
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
1
7917 
9
 
758
8
 
328
2
 
301
10
 
108

Length

Max length2
Median length1
Mean length1.0114747
Min length1

Characters and Unicode

Total characters9520
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row9
5th row1

Common Values

ValueCountFrequency (%)
1 7917
84.1%
9 758
 
8.1%
8 328
 
3.5%
2 301
 
3.2%
10 108
 
1.1%

Length

2023-03-22T15:15:25.159106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:25.309230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 7917
84.1%
9 758
 
8.1%
8 328
 
3.5%
2 301
 
3.2%
10 108
 
1.1%

Most occurring characters

ValueCountFrequency (%)
1 8025
84.3%
9 758
 
8.0%
8 328
 
3.4%
2 301
 
3.2%
0 108
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9520
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8025
84.3%
9 758
 
8.0%
8 328
 
3.4%
2 301
 
3.2%
0 108
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9520
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8025
84.3%
9 758
 
8.0%
8 328
 
3.4%
2 301
 
3.2%
0 108
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8025
84.3%
9 758
 
8.0%
8 328
 
3.4%
2 301
 
3.2%
0 108
 
1.1%

q21
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
9
5005 
1
3288 
2
654 
8
 
328
10
 
137

Length

Max length2
Median length1
Mean length1.0145559
Min length1

Characters and Unicode

Total characters9549
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row9
4th row9
5th row9

Common Values

ValueCountFrequency (%)
9 5005
53.2%
1 3288
34.9%
2 654
 
6.9%
8 328
 
3.5%
10 137
 
1.5%

Length

2023-03-22T15:15:25.418613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:25.565557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
9 5005
53.2%
1 3288
34.9%
2 654
 
6.9%
8 328
 
3.5%
10 137
 
1.5%

Most occurring characters

ValueCountFrequency (%)
9 5005
52.4%
1 3425
35.9%
2 654
 
6.8%
8 328
 
3.4%
0 137
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9549
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 5005
52.4%
1 3425
35.9%
2 654
 
6.8%
8 328
 
3.4%
0 137
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9549
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 5005
52.4%
1 3425
35.9%
2 654
 
6.8%
8 328
 
3.4%
0 137
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9549
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 5005
52.4%
1 3425
35.9%
2 654
 
6.8%
8 328
 
3.4%
0 137
 
1.4%

qp1
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8589035
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:25.674291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.78142716
Coefficient of variation (CV)0.099432084
Kurtosis33.624004
Mean7.8589035
Median Absolute Deviation (MAD)0
Skewness-5.7299087
Sum73968
Variance0.6106284
MonotonicityNot monotonic
2023-03-22T15:15:25.772113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
8 9084
96.5%
4 130
 
1.4%
5 95
 
1.0%
3 67
 
0.7%
1 21
 
0.2%
2 8
 
0.1%
9 7
 
0.1%
ValueCountFrequency (%)
1 21
 
0.2%
2 8
 
0.1%
3 67
 
0.7%
4 130
 
1.4%
5 95
 
1.0%
8 9084
96.5%
9 7
 
0.1%
ValueCountFrequency (%)
9 7
 
0.1%
8 9084
96.5%
5 95
 
1.0%
4 130
 
1.4%
3 67
 
0.7%
2 8
 
0.1%
1 21
 
0.2%

qp2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9084 
2
 
196
3
 
93
1
 
34
9
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9084
96.5%
2 196
 
2.1%
3 93
 
1.0%
1 34
 
0.4%
9 5
 
0.1%

Length

2023-03-22T15:15:25.913547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:26.039916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9084
96.5%
2 196
 
2.1%
3 93
 
1.0%
1 34
 
0.4%
9 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9084
96.5%
2 196
 
2.1%
3 93
 
1.0%
1 34
 
0.4%
9 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9084
96.5%
2 196
 
2.1%
3 93
 
1.0%
1 34
 
0.4%
9 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9084
96.5%
2 196
 
2.1%
3 93
 
1.0%
1 34
 
0.4%
9 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9084
96.5%
2 196
 
2.1%
3 93
 
1.0%
1 34
 
0.4%
9 5
 
0.1%

qp3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9084 
1
 
294
0
 
34

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9084
96.5%
1 294
 
3.1%
0 34
 
0.4%

Length

2023-03-22T15:15:26.142149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:26.289687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9084
96.5%
1 294
 
3.1%
0 34
 
0.4%

Most occurring characters

ValueCountFrequency (%)
8 9084
96.5%
1 294
 
3.1%
0 34
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9084
96.5%
1 294
 
3.1%
0 34
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9084
96.5%
1 294
 
3.1%
0 34
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9084
96.5%
1 294
 
3.1%
0 34
 
0.4%

qp4
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8291543
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:26.373834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.96155402
Coefficient of variation (CV)0.1228171
Kurtosis29.217405
Mean7.8291543
Median Absolute Deviation (MAD)0
Skewness-5.5380138
Sum73688
Variance0.92458613
MonotonicityNot monotonic
2023-03-22T15:15:26.498806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 9118
96.9%
3 140
 
1.5%
2 120
 
1.3%
1 19
 
0.2%
4 14
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
1 19
 
0.2%
2 120
 
1.3%
3 140
 
1.5%
4 14
 
0.1%
8 9118
96.9%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 9118
96.9%
4 14
 
0.1%
3 140
 
1.5%
2 120
 
1.3%
1 19
 
0.2%

qp5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9393 
2
 
15
1
 
3
9
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9393
99.8%
2 15
 
0.2%
1 3
 
< 0.1%
9 1
 
< 0.1%

Length

2023-03-22T15:15:26.641565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:26.774861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9393
99.8%
2 15
 
0.2%
1 3
 
< 0.1%
9 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 9393
99.8%
2 15
 
0.2%
1 3
 
< 0.1%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9393
99.8%
2 15
 
0.2%
1 3
 
< 0.1%
9 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9393
99.8%
2 15
 
0.2%
1 3
 
< 0.1%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9393
99.8%
2 15
 
0.2%
1 3
 
< 0.1%
9 1
 
< 0.1%

qp6
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9272 
2
 
77
1
 
63

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9272
98.5%
2 77
 
0.8%
1 63
 
0.7%

Length

2023-03-22T15:15:26.888571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:27.013226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9272
98.5%
2 77
 
0.8%
1 63
 
0.7%

Most occurring characters

ValueCountFrequency (%)
8 9272
98.5%
2 77
 
0.8%
1 63
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9272
98.5%
2 77
 
0.8%
1 63
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9272
98.5%
2 77
 
0.8%
1 63
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9272
98.5%
2 77
 
0.8%
1 63
 
0.7%

qp7
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8313855
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:27.107969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.96200305
Coefficient of variation (CV)0.12283945
Kurtosis30.019492
Mean7.8313855
Median Absolute Deviation (MAD)0
Skewness-5.6024802
Sum73709
Variance0.92544987
MonotonicityNot monotonic
2023-03-22T15:15:27.252614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 9118
96.9%
2 125
 
1.3%
3 119
 
1.3%
1 24
 
0.3%
4 20
 
0.2%
9 6
 
0.1%
ValueCountFrequency (%)
1 24
 
0.3%
2 125
 
1.3%
3 119
 
1.3%
4 20
 
0.2%
8 9118
96.9%
9 6
 
0.1%
ValueCountFrequency (%)
9 6
 
0.1%
8 9118
96.9%
4 20
 
0.2%
3 119
 
1.3%
2 125
 
1.3%
1 24
 
0.3%

qp8
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9388 
2
 
18
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9388
99.7%
2 18
 
0.2%
1 6
 
0.1%

Length

2023-03-22T15:15:27.620605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:28.106323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9388
99.7%
2 18
 
0.2%
1 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9388
99.7%
2 18
 
0.2%
1 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9388
99.7%
2 18
 
0.2%
1 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9388
99.7%
2 18
 
0.2%
1 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9388
99.7%
2 18
 
0.2%
1 6
 
0.1%

qp9
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9293 
2
 
69
1
 
50

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9293
98.7%
2 69
 
0.7%
1 50
 
0.5%

Length

2023-03-22T15:15:28.436422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:28.850314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9293
98.7%
2 69
 
0.7%
1 50
 
0.5%

Most occurring characters

ValueCountFrequency (%)
8 9293
98.7%
2 69
 
0.7%
1 50
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9293
98.7%
2 69
 
0.7%
1 50
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9293
98.7%
2 69
 
0.7%
1 50
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9293
98.7%
2 69
 
0.7%
1 50
 
0.5%

qp10
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8306417
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:29.119593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.95894044
Coefficient of variation (CV)0.12246001
Kurtosis29.633731
Mean7.8306417
Median Absolute Deviation (MAD)0
Skewness-5.5713713
Sum73702
Variance0.91956678
MonotonicityNot monotonic
2023-03-22T15:15:29.327693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 9118
96.9%
2 127
 
1.3%
3 122
 
1.3%
4 23
 
0.2%
1 19
 
0.2%
9 3
 
< 0.1%
ValueCountFrequency (%)
1 19
 
0.2%
2 127
 
1.3%
3 122
 
1.3%
4 23
 
0.2%
8 9118
96.9%
9 3
 
< 0.1%
ValueCountFrequency (%)
9 3
 
< 0.1%
8 9118
96.9%
4 23
 
0.2%
3 122
 
1.3%
2 127
 
1.3%
1 19
 
0.2%

qp11
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9393 
2
 
10
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9393
99.8%
2 10
 
0.1%
1 9
 
0.1%

Length

2023-03-22T15:15:29.458493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:29.582533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9393
99.8%
2 10
 
0.1%
1 9
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9393
99.8%
2 10
 
0.1%
1 9
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9393
99.8%
2 10
 
0.1%
1 9
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9393
99.8%
2 10
 
0.1%
1 9
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9393
99.8%
2 10
 
0.1%
1 9
 
0.1%

qp12
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9290 
2
 
68
1
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9290
98.7%
2 68
 
0.7%
1 54
 
0.6%

Length

2023-03-22T15:15:29.709655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:29.837304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9290
98.7%
2 68
 
0.7%
1 54
 
0.6%

Most occurring characters

ValueCountFrequency (%)
8 9290
98.7%
2 68
 
0.7%
1 54
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9290
98.7%
2 68
 
0.7%
1 54
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9290
98.7%
2 68
 
0.7%
1 54
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9290
98.7%
2 68
 
0.7%
1 54
 
0.6%

qp13
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8339354
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:29.957983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.94708816
Coefficient of variation (CV)0.12089558
Kurtosis30.382633
Mean7.8339354
Median Absolute Deviation (MAD)0
Skewness-5.625662
Sum73733
Variance0.89697599
MonotonicityNot monotonic
2023-03-22T15:15:30.065694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 9118
96.9%
2 116
 
1.2%
3 114
 
1.2%
4 37
 
0.4%
1 22
 
0.2%
9 5
 
0.1%
ValueCountFrequency (%)
1 22
 
0.2%
2 116
 
1.2%
3 114
 
1.2%
4 37
 
0.4%
8 9118
96.9%
9 5
 
0.1%
ValueCountFrequency (%)
9 5
 
0.1%
8 9118
96.9%
4 37
 
0.4%
3 114
 
1.2%
2 116
 
1.2%
1 22
 
0.2%

qp14
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9390 
1
 
11
2
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9390
99.8%
1 11
 
0.1%
2 11
 
0.1%

Length

2023-03-22T15:15:30.200333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:30.329986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9390
99.8%
1 11
 
0.1%
2 11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9390
99.8%
1 11
 
0.1%
2 11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9390
99.8%
1 11
 
0.1%
2 11
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9390
99.8%
1 11
 
0.1%
2 11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9390
99.8%
1 11
 
0.1%
2 11
 
0.1%

qp15
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9298 
2
 
77
1
 
36
9
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9298
98.8%
2 77
 
0.8%
1 36
 
0.4%
9 1
 
< 0.1%

Length

2023-03-22T15:15:30.451669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:30.585308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9298
98.8%
2 77
 
0.8%
1 36
 
0.4%
9 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 9298
98.8%
2 77
 
0.8%
1 36
 
0.4%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9298
98.8%
2 77
 
0.8%
1 36
 
0.4%
9 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9298
98.8%
2 77
 
0.8%
1 36
 
0.4%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9298
98.8%
2 77
 
0.8%
1 36
 
0.4%
9 1
 
< 0.1%

qp16
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8345729
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:30.684041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.93716236
Coefficient of variation (CV)0.11961882
Kurtosis29.761634
Mean7.8345729
Median Absolute Deviation (MAD)0
Skewness-5.5795659
Sum73739
Variance0.8782733
MonotonicityNot monotonic
2023-03-22T15:15:30.793746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 9118
96.9%
3 135
 
1.4%
2 111
 
1.2%
4 32
 
0.3%
1 13
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
1 13
 
0.1%
2 111
 
1.2%
3 135
 
1.4%
4 32
 
0.3%
8 9118
96.9%
9 3
 
< 0.1%
ValueCountFrequency (%)
9 3
 
< 0.1%
8 9118
96.9%
4 32
 
0.3%
3 135
 
1.4%
2 111
 
1.2%
1 13
 
0.1%

qp17
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9399 
1
 
7
2
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9399
99.9%
1 7
 
0.1%
2 6
 
0.1%

Length

2023-03-22T15:15:30.947336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:31.073035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9399
99.9%
1 7
 
0.1%
2 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9399
99.9%
1 7
 
0.1%
2 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9399
99.9%
1 7
 
0.1%
2 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9399
99.9%
1 7
 
0.1%
2 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9399
99.9%
1 7
 
0.1%
2 6
 
0.1%

qp18
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9277 
2
 
91
1
 
44

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9277
98.6%
2 91
 
1.0%
1 44
 
0.5%

Length

2023-03-22T15:15:31.185710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:31.429048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9277
98.6%
2 91
 
1.0%
1 44
 
0.5%

Most occurring characters

ValueCountFrequency (%)
8 9277
98.6%
2 91
 
1.0%
1 44
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9277
98.6%
2 91
 
1.0%
1 44
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9277
98.6%
2 91
 
1.0%
1 44
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9277
98.6%
2 91
 
1.0%
1 44
 
0.5%

qp19
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8312792
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:31.641485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.95182388
Coefficient of variation (CV)0.1215413
Kurtosis29.782664
Mean7.8312792
Median Absolute Deviation (MAD)0
Skewness-5.5764707
Sum73708
Variance0.9059687
MonotonicityNot monotonic
2023-03-22T15:15:31.942676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 9118
96.9%
3 134
 
1.4%
2 107
 
1.1%
4 29
 
0.3%
1 23
 
0.2%
9 1
 
< 0.1%
ValueCountFrequency (%)
1 23
 
0.2%
2 107
 
1.1%
3 134
 
1.4%
4 29
 
0.3%
8 9118
96.9%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 9118
96.9%
4 29
 
0.3%
3 134
 
1.4%
2 107
 
1.1%
1 23
 
0.2%

qp20
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9389 
1
 
13
2
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9389
99.8%
1 13
 
0.1%
2 10
 
0.1%

Length

2023-03-22T15:15:32.315681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:32.619871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9389
99.8%
1 13
 
0.1%
2 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9389
99.8%
1 13
 
0.1%
2 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9389
99.8%
1 13
 
0.1%
2 10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9389
99.8%
1 13
 
0.1%
2 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9389
99.8%
1 13
 
0.1%
2 10
 
0.1%

qp21
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9278 
2
 
95
1
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9278
98.6%
2 95
 
1.0%
1 39
 
0.4%

Length

2023-03-22T15:15:32.873193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:33.159427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9278
98.6%
2 95
 
1.0%
1 39
 
0.4%

Most occurring characters

ValueCountFrequency (%)
8 9278
98.6%
2 95
 
1.0%
1 39
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9278
98.6%
2 95
 
1.0%
1 39
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9278
98.6%
2 95
 
1.0%
1 39
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9278
98.6%
2 95
 
1.0%
1 39
 
0.4%

qp22
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8351041
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:33.310049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.93708592
Coefficient of variation (CV)0.11960095
Kurtosis30.077441
Mean7.8351041
Median Absolute Deviation (MAD)0
Skewness-5.6014997
Sum73744
Variance0.87813002
MonotonicityNot monotonic
2023-03-22T15:15:33.494527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 9118
96.9%
3 138
 
1.5%
2 102
 
1.1%
4 32
 
0.3%
1 18
 
0.2%
9 4
 
< 0.1%
ValueCountFrequency (%)
1 18
 
0.2%
2 102
 
1.1%
3 138
 
1.5%
4 32
 
0.3%
8 9118
96.9%
9 4
 
< 0.1%
ValueCountFrequency (%)
9 4
 
< 0.1%
8 9118
96.9%
4 32
 
0.3%
3 138
 
1.5%
2 102
 
1.1%
1 18
 
0.2%

qp23
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9394 
1
 
11
2
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9394
99.8%
1 11
 
0.1%
2 7
 
0.1%

Length

2023-03-22T15:15:33.729897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:33.892464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9394
99.8%
1 11
 
0.1%
2 7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9394
99.8%
1 11
 
0.1%
2 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9394
99.8%
1 11
 
0.1%
2 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9394
99.8%
1 11
 
0.1%
2 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9394
99.8%
1 11
 
0.1%
2 7
 
0.1%

qp24
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9274 
2
 
92
1
 
46

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9274
98.5%
2 92
 
1.0%
1 46
 
0.5%

Length

2023-03-22T15:15:34.033086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:34.174722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9274
98.5%
2 92
 
1.0%
1 46
 
0.5%

Most occurring characters

ValueCountFrequency (%)
8 9274
98.5%
2 92
 
1.0%
1 46
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9274
98.5%
2 92
 
1.0%
1 46
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9274
98.5%
2 92
 
1.0%
1 46
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9274
98.5%
2 92
 
1.0%
1 46
 
0.5%

qp25
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8363791
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:34.269689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.92956858
Coefficient of variation (CV)0.11862221
Kurtosis30.426277
Mean7.8363791
Median Absolute Deviation (MAD)0
Skewness-5.6251567
Sum73756
Variance0.86409774
MonotonicityNot monotonic
2023-03-22T15:15:34.395351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 9118
96.9%
3 113
 
1.2%
2 109
 
1.2%
4 53
 
0.6%
1 16
 
0.2%
9 3
 
< 0.1%
ValueCountFrequency (%)
1 16
 
0.2%
2 109
 
1.2%
3 113
 
1.2%
4 53
 
0.6%
8 9118
96.9%
9 3
 
< 0.1%
ValueCountFrequency (%)
9 3
 
< 0.1%
8 9118
96.9%
4 53
 
0.6%
3 113
 
1.2%
2 109
 
1.2%
1 16
 
0.2%

qp26
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9396 
2
 
9
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9396
99.8%
2 9
 
0.1%
1 7
 
0.1%

Length

2023-03-22T15:15:34.533981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:34.683589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9396
99.8%
2 9
 
0.1%
1 7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9396
99.8%
2 9
 
0.1%
1 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9396
99.8%
2 9
 
0.1%
1 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9396
99.8%
2 9
 
0.1%
1 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9396
99.8%
2 9
 
0.1%
1 7
 
0.1%

qp27
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9299 
2
 
89
1
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9299
98.8%
2 89
 
0.9%
1 24
 
0.3%

Length

2023-03-22T15:15:34.786336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:34.939896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9299
98.8%
2 89
 
0.9%
1 24
 
0.3%

Most occurring characters

ValueCountFrequency (%)
8 9299
98.8%
2 89
 
0.9%
1 24
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9299
98.8%
2 89
 
0.9%
1 24
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9299
98.8%
2 89
 
0.9%
1 24
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9299
98.8%
2 89
 
0.9%
1 24
 
0.3%

qp28
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8429664
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:35.037636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.89994063
Coefficient of variation (CV)0.11474493
Kurtosis31.173378
Mean7.8429664
Median Absolute Deviation (MAD)0
Skewness-5.6762878
Sum73818
Variance0.80989314
MonotonicityNot monotonic
2023-03-22T15:15:35.181267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 9118
96.9%
3 128
 
1.4%
2 78
 
0.8%
4 66
 
0.7%
1 16
 
0.2%
9 6
 
0.1%
ValueCountFrequency (%)
1 16
 
0.2%
2 78
 
0.8%
3 128
 
1.4%
4 66
 
0.7%
8 9118
96.9%
9 6
 
0.1%
ValueCountFrequency (%)
9 6
 
0.1%
8 9118
96.9%
4 66
 
0.7%
3 128
 
1.4%
2 78
 
0.8%
1 16
 
0.2%

qp29
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9396 
2
 
11
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9396
99.8%
2 11
 
0.1%
1 5
 
0.1%

Length

2023-03-22T15:15:35.767714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:36.044961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9396
99.8%
2 11
 
0.1%
1 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9396
99.8%
2 11
 
0.1%
1 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9396
99.8%
2 11
 
0.1%
1 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9396
99.8%
2 11
 
0.1%
1 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9396
99.8%
2 11
 
0.1%
1 5
 
0.1%

qp30
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9284 
2
 
95
1
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9284
98.6%
2 95
 
1.0%
1 33
 
0.4%

Length

2023-03-22T15:15:36.266903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:36.548086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9284
98.6%
2 95
 
1.0%
1 33
 
0.4%

Most occurring characters

ValueCountFrequency (%)
8 9284
98.6%
2 95
 
1.0%
1 33
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9284
98.6%
2 95
 
1.0%
1 33
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9284
98.6%
2 95
 
1.0%
1 33
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9284
98.6%
2 95
 
1.0%
1 33
 
0.4%

qp31
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8386103
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:36.751170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.92359529
Coefficient of variation (CV)0.11782641
Kurtosis30.778445
Mean7.8386103
Median Absolute Deviation (MAD)0
Skewness-5.6518438
Sum73777
Variance0.85302827
MonotonicityNot monotonic
2023-03-22T15:15:37.051086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 9118
96.9%
3 128
 
1.4%
2 94
 
1.0%
4 47
 
0.5%
1 19
 
0.2%
9 6
 
0.1%
ValueCountFrequency (%)
1 19
 
0.2%
2 94
 
1.0%
3 128
 
1.4%
4 47
 
0.5%
8 9118
96.9%
9 6
 
0.1%
ValueCountFrequency (%)
9 6
 
0.1%
8 9118
96.9%
4 47
 
0.5%
3 128
 
1.4%
2 94
 
1.0%
1 19
 
0.2%

qp32
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9393 
2
 
12
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9393
99.8%
2 12
 
0.1%
1 7
 
0.1%

Length

2023-03-22T15:15:37.356250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:37.639420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9393
99.8%
2 12
 
0.1%
1 7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9393
99.8%
2 12
 
0.1%
1 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9393
99.8%
2 12
 
0.1%
1 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9393
99.8%
2 12
 
0.1%
1 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9393
99.8%
2 12
 
0.1%
1 7
 
0.1%

qp33
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9284 
2
 
95
1
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9284
98.6%
2 95
 
1.0%
1 33
 
0.4%

Length

2023-03-22T15:15:37.873741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:38.154939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9284
98.6%
2 95
 
1.0%
1 33
 
0.4%

Most occurring characters

ValueCountFrequency (%)
8 9284
98.6%
2 95
 
1.0%
1 33
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9284
98.6%
2 95
 
1.0%
1 33
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9284
98.6%
2 95
 
1.0%
1 33
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9284
98.6%
2 95
 
1.0%
1 33
 
0.4%

qp34
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8356354
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:38.295536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.93394229
Coefficient of variation (CV)0.11919165
Kurtosis30.461726
Mean7.8356354
Median Absolute Deviation (MAD)0
Skewness-5.627708
Sum73749
Variance0.87224821
MonotonicityNot monotonic
2023-03-22T15:15:38.452024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 9118
96.9%
2 111
 
1.2%
3 110
 
1.2%
4 52
 
0.6%
1 18
 
0.2%
9 3
 
< 0.1%
ValueCountFrequency (%)
1 18
 
0.2%
2 111
 
1.2%
3 110
 
1.2%
4 52
 
0.6%
8 9118
96.9%
9 3
 
< 0.1%
ValueCountFrequency (%)
9 3
 
< 0.1%
8 9118
96.9%
4 52
 
0.6%
3 110
 
1.2%
2 111
 
1.2%
1 18
 
0.2%

qp35
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9394 
2
 
10
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9394
99.8%
2 10
 
0.1%
1 8
 
0.1%

Length

2023-03-22T15:15:38.610835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:38.738477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9394
99.8%
2 10
 
0.1%
1 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9394
99.8%
2 10
 
0.1%
1 8
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9394
99.8%
2 10
 
0.1%
1 8
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9394
99.8%
2 10
 
0.1%
1 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9394
99.8%
2 10
 
0.1%
1 8
 
0.1%

qp36
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9302 
2
 
81
1
 
28
9
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9302
98.8%
2 81
 
0.9%
1 28
 
0.3%
9 1
 
< 0.1%

Length

2023-03-22T15:15:38.838230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:38.963201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9302
98.8%
2 81
 
0.9%
1 28
 
0.3%
9 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 9302
98.8%
2 81
 
0.9%
1 28
 
0.3%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9302
98.8%
2 81
 
0.9%
1 28
 
0.3%
9 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9302
98.8%
2 81
 
0.9%
1 28
 
0.3%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9302
98.8%
2 81
 
0.9%
1 28
 
0.3%
9 1
 
< 0.1%

qp37
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8341479
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:39.056929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.93708724
Coefficient of variation (CV)0.11961572
Kurtosis30.103663
Mean7.8341479
Median Absolute Deviation (MAD)0
Skewness-5.5994797
Sum73735
Variance0.87813249
MonotonicityNot monotonic
2023-03-22T15:15:39.169113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 9118
96.9%
3 120
 
1.3%
2 108
 
1.1%
4 47
 
0.5%
1 18
 
0.2%
9 1
 
< 0.1%
ValueCountFrequency (%)
1 18
 
0.2%
2 108
 
1.1%
3 120
 
1.3%
4 47
 
0.5%
8 9118
96.9%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 9118
96.9%
4 47
 
0.5%
3 120
 
1.3%
2 108
 
1.1%
1 18
 
0.2%

qp38
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9394 
1
 
13
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9394
99.8%
1 13
 
0.1%
2 5
 
0.1%

Length

2023-03-22T15:15:39.319007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:39.469700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9394
99.8%
1 13
 
0.1%
2 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9394
99.8%
1 13
 
0.1%
2 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9394
99.8%
1 13
 
0.1%
2 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9394
99.8%
1 13
 
0.1%
2 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9394
99.8%
1 13
 
0.1%
2 5
 
0.1%

qp39
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9292 
2
 
91
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9292
98.7%
2 91
 
1.0%
1 29
 
0.3%

Length

2023-03-22T15:15:39.574358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:39.696638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9292
98.7%
2 91
 
1.0%
1 29
 
0.3%

Most occurring characters

ValueCountFrequency (%)
8 9292
98.7%
2 91
 
1.0%
1 29
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9292
98.7%
2 91
 
1.0%
1 29
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9292
98.7%
2 91
 
1.0%
1 29
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9292
98.7%
2 91
 
1.0%
1 29
 
0.3%

qp40
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9118 
3
 
120
2
 
102
4
 
62
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9118
96.9%
3 120
 
1.3%
2 102
 
1.1%
4 62
 
0.7%
1 10
 
0.1%

Length

2023-03-22T15:15:39.790592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:39.919224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9118
96.9%
3 120
 
1.3%
2 102
 
1.1%
4 62
 
0.7%
1 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9118
96.9%
3 120
 
1.3%
2 102
 
1.1%
4 62
 
0.7%
1 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9118
96.9%
3 120
 
1.3%
2 102
 
1.1%
4 62
 
0.7%
1 10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9118
96.9%
3 120
 
1.3%
2 102
 
1.1%
4 62
 
0.7%
1 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9118
96.9%
3 120
 
1.3%
2 102
 
1.1%
4 62
 
0.7%
1 10
 
0.1%

qp41
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9402 
1
 
5
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9402
99.9%
1 5
 
0.1%
2 5
 
0.1%

Length

2023-03-22T15:15:40.029221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:40.189153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9402
99.9%
1 5
 
0.1%
2 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9402
99.9%
1 5
 
0.1%
2 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9402
99.9%
1 5
 
0.1%
2 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9402
99.9%
1 5
 
0.1%
2 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9402
99.9%
1 5
 
0.1%
2 5
 
0.1%

qp42
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9292 
2
 
97
1
 
22
9
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9292
98.7%
2 97
 
1.0%
1 22
 
0.2%
9 1
 
< 0.1%

Length

2023-03-22T15:15:40.448724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:40.809705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9292
98.7%
2 97
 
1.0%
1 22
 
0.2%
9 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 9292
98.7%
2 97
 
1.0%
1 22
 
0.2%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9292
98.7%
2 97
 
1.0%
1 22
 
0.2%
9 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9292
98.7%
2 97
 
1.0%
1 22
 
0.2%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9292
98.7%
2 97
 
1.0%
1 22
 
0.2%
9 1
 
< 0.1%

qp43
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8428602
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:41.054573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8923928
Coefficient of variation (CV)0.1137841
Kurtosis30.363175
Mean7.8428602
Median Absolute Deviation (MAD)0
Skewness-5.6194235
Sum73817
Variance0.79636491
MonotonicityNot monotonic
2023-03-22T15:15:41.320138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 9118
96.9%
3 130
 
1.4%
2 85
 
0.9%
4 70
 
0.7%
1 6
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
1 6
 
0.1%
2 85
 
0.9%
3 130
 
1.4%
4 70
 
0.7%
8 9118
96.9%
9 3
 
< 0.1%
ValueCountFrequency (%)
9 3
 
< 0.1%
8 9118
96.9%
4 70
 
0.7%
3 130
 
1.4%
2 85
 
0.9%
1 6
 
0.1%

qp44
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9406 
1
 
5
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9406
99.9%
1 5
 
0.1%
2 1
 
< 0.1%

Length

2023-03-22T15:15:41.668247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:42.005047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9406
99.9%
1 5
 
0.1%
2 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 9406
99.9%
1 5
 
0.1%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9406
99.9%
1 5
 
0.1%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9406
99.9%
1 5
 
0.1%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9406
99.9%
1 5
 
0.1%
2 1
 
< 0.1%

qp45
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9282 
2
 
102
1
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9282
98.6%
2 102
 
1.1%
1 28
 
0.3%

Length

2023-03-22T15:15:42.257686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:42.378904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9282
98.6%
2 102
 
1.1%
1 28
 
0.3%

Most occurring characters

ValueCountFrequency (%)
8 9282
98.6%
2 102
 
1.1%
1 28
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9282
98.6%
2 102
 
1.1%
1 28
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9282
98.6%
2 102
 
1.1%
1 28
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9282
98.6%
2 102
 
1.1%
1 28
 
0.3%

qp46
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8363791
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:42.494019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.93185196
Coefficient of variation (CV)0.11891359
Kurtosis30.48383
Mean7.8363791
Median Absolute Deviation (MAD)0
Skewness-5.6317645
Sum73756
Variance0.86834808
MonotonicityNot monotonic
2023-03-22T15:15:42.602178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 9118
96.9%
2 117
 
1.2%
3 105
 
1.1%
4 53
 
0.6%
1 15
 
0.2%
9 4
 
< 0.1%
ValueCountFrequency (%)
1 15
 
0.2%
2 117
 
1.2%
3 105
 
1.1%
4 53
 
0.6%
8 9118
96.9%
9 4
 
< 0.1%
ValueCountFrequency (%)
9 4
 
< 0.1%
8 9118
96.9%
4 53
 
0.6%
3 105
 
1.1%
2 117
 
1.2%
1 15
 
0.2%

qp47
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9397 
2
 
9
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9397
99.8%
2 9
 
0.1%
1 6
 
0.1%

Length

2023-03-22T15:15:42.730727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:42.848710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9397
99.8%
2 9
 
0.1%
1 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9397
99.8%
2 9
 
0.1%
1 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9397
99.8%
2 9
 
0.1%
1 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9397
99.8%
2 9
 
0.1%
1 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9397
99.8%
2 9
 
0.1%
1 6
 
0.1%

qp48
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9307 
2
 
85
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9307
98.9%
2 85
 
0.9%
1 20
 
0.2%

Length

2023-03-22T15:15:42.949051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:43.078671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9307
98.9%
2 85
 
0.9%
1 20
 
0.2%

Most occurring characters

ValueCountFrequency (%)
8 9307
98.9%
2 85
 
0.9%
1 20
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9307
98.9%
2 85
 
0.9%
1 20
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9307
98.9%
2 85
 
0.9%
1 20
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9307
98.9%
2 85
 
0.9%
1 20
 
0.2%

qp49
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8353166
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.7 KiB
2023-03-22T15:15:43.174905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median8
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.93700989
Coefficient of variation (CV)0.11958801
Kurtosis30.296289
Mean7.8353166
Median Absolute Deviation (MAD)0
Skewness-5.6181196
Sum73746
Variance0.87798754
MonotonicityNot monotonic
2023-03-22T15:15:43.282515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 9118
96.9%
3 117
 
1.2%
2 113
 
1.2%
4 43
 
0.5%
1 17
 
0.2%
9 4
 
< 0.1%
ValueCountFrequency (%)
1 17
 
0.2%
2 113
 
1.2%
3 117
 
1.2%
4 43
 
0.5%
8 9118
96.9%
9 4
 
< 0.1%
ValueCountFrequency (%)
9 4
 
< 0.1%
8 9118
96.9%
4 43
 
0.5%
3 117
 
1.2%
2 113
 
1.2%
1 17
 
0.2%

qp50
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9395 
2
 
10
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9395
99.8%
2 10
 
0.1%
1 7
 
0.1%

Length

2023-03-22T15:15:43.413762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:43.534831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9395
99.8%
2 10
 
0.1%
1 7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9395
99.8%
2 10
 
0.1%
1 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9395
99.8%
2 10
 
0.1%
1 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9395
99.8%
2 10
 
0.1%
1 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9395
99.8%
2 10
 
0.1%
1 7
 
0.1%

qp51
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9295 
2
 
74
1
 
43

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9295
98.8%
2 74
 
0.8%
1 43
 
0.5%

Length

2023-03-22T15:15:43.628565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:43.769157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9295
98.8%
2 74
 
0.8%
1 43
 
0.5%

Most occurring characters

ValueCountFrequency (%)
8 9295
98.8%
2 74
 
0.8%
1 43
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9295
98.8%
2 74
 
0.8%
1 43
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9295
98.8%
2 74
 
0.8%
1 43
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9295
98.8%
2 74
 
0.8%
1 43
 
0.5%

qp52
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9118 
0
 
213
1
 
75
9
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9118
96.9%
0 213
 
2.3%
1 75
 
0.8%
9 6
 
0.1%

Length

2023-03-22T15:15:43.861572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:43.998420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9118
96.9%
0 213
 
2.3%
1 75
 
0.8%
9 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9118
96.9%
0 213
 
2.3%
1 75
 
0.8%
9 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9118
96.9%
0 213
 
2.3%
1 75
 
0.8%
9 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9118
96.9%
0 213
 
2.3%
1 75
 
0.8%
9 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9118
96.9%
0 213
 
2.3%
1 75
 
0.8%
9 6
 
0.1%

qp53
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9118 
0
 
234
1
 
38
9
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9118
96.9%
0 234
 
2.5%
1 38
 
0.4%
9 22
 
0.2%

Length

2023-03-22T15:15:44.105506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:44.252336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9118
96.9%
0 234
 
2.5%
1 38
 
0.4%
9 22
 
0.2%

Most occurring characters

ValueCountFrequency (%)
8 9118
96.9%
0 234
 
2.5%
1 38
 
0.4%
9 22
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9118
96.9%
0 234
 
2.5%
1 38
 
0.4%
9 22
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9118
96.9%
0 234
 
2.5%
1 38
 
0.4%
9 22
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9118
96.9%
0 234
 
2.5%
1 38
 
0.4%
9 22
 
0.2%

qp54
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9118 
1
 
180
0
 
114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9118
96.9%
1 180
 
1.9%
0 114
 
1.2%

Length

2023-03-22T15:15:44.475702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:44.752682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9118
96.9%
1 180
 
1.9%
0 114
 
1.2%

Most occurring characters

ValueCountFrequency (%)
8 9118
96.9%
1 180
 
1.9%
0 114
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9118
96.9%
1 180
 
1.9%
0 114
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9118
96.9%
1 180
 
1.9%
0 114
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9118
96.9%
1 180
 
1.9%
0 114
 
1.2%

qp55
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
8
9118 
0
 
190
1
 
98
9
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 9118
96.9%
0 190
 
2.0%
1 98
 
1.0%
9 6
 
0.1%

Length

2023-03-22T15:15:44.982573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:45.312366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
8 9118
96.9%
0 190
 
2.0%
1 98
 
1.0%
9 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
8 9118
96.9%
0 190
 
2.0%
1 98
 
1.0%
9 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 9118
96.9%
0 190
 
2.0%
1 98
 
1.0%
9 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 9118
96.9%
0 190
 
2.0%
1 98
 
1.0%
9 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 9118
96.9%
0 190
 
2.0%
1 98
 
1.0%
9 6
 
0.1%

r1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
0
8502 
1
910 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8502
90.3%
1 910
 
9.7%

Length

2023-03-22T15:15:45.587543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:45.863645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 8502
90.3%
1 910
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 8502
90.3%
1 910
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8502
90.3%
1 910
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8502
90.3%
1 910
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8502
90.3%
1 910
 
9.7%

r2
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
0
6032 
1
2438 
8
910 
9
 
32

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6032
64.1%
1 2438
25.9%
8 910
 
9.7%
9 32
 
0.3%

Length

2023-03-22T15:15:46.113746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:46.426373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 6032
64.1%
1 2438
25.9%
8 910
 
9.7%
9 32
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 6032
64.1%
1 2438
25.9%
8 910
 
9.7%
9 32
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6032
64.1%
1 2438
25.9%
8 910
 
9.7%
9 32
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6032
64.1%
1 2438
25.9%
8 910
 
9.7%
9 32
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6032
64.1%
1 2438
25.9%
8 910
 
9.7%
9 32
 
0.3%

r3
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
1
4711 
0
3767 
8
910 
9
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4711
50.1%
0 3767
40.0%
8 910
 
9.7%
9 24
 
0.3%

Length

2023-03-22T15:15:46.728508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:47.032224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 4711
50.1%
0 3767
40.0%
8 910
 
9.7%
9 24
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1 4711
50.1%
0 3767
40.0%
8 910
 
9.7%
9 24
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4711
50.1%
0 3767
40.0%
8 910
 
9.7%
9 24
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4711
50.1%
0 3767
40.0%
8 910
 
9.7%
9 24
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4711
50.1%
0 3767
40.0%
8 910
 
9.7%
9 24
 
0.3%

r4
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
1
4381 
0
4104 
8
910 
9
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 4381
46.5%
0 4104
43.6%
8 910
 
9.7%
9 17
 
0.2%

Length

2023-03-22T15:15:47.282905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:47.407878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 4381
46.5%
0 4104
43.6%
8 910
 
9.7%
9 17
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 4381
46.5%
0 4104
43.6%
8 910
 
9.7%
9 17
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4381
46.5%
0 4104
43.6%
8 910
 
9.7%
9 17
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4381
46.5%
0 4104
43.6%
8 910
 
9.7%
9 17
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4381
46.5%
0 4104
43.6%
8 910
 
9.7%
9 17
 
0.2%

r5
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
1
6526 
0
1925 
8
910 
9
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 6526
69.3%
0 1925
 
20.5%
8 910
 
9.7%
9 51
 
0.5%

Length

2023-03-22T15:15:47.517225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:47.642620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 6526
69.3%
0 1925
 
20.5%
8 910
 
9.7%
9 51
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1 6526
69.3%
0 1925
 
20.5%
8 910
 
9.7%
9 51
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6526
69.3%
0 1925
 
20.5%
8 910
 
9.7%
9 51
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6526
69.3%
0 1925
 
20.5%
8 910
 
9.7%
9 51
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6526
69.3%
0 1925
 
20.5%
8 910
 
9.7%
9 51
 
0.5%

r6
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
0
5506 
1
2984 
8
910 
9
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5506
58.5%
1 2984
31.7%
8 910
 
9.7%
9 12
 
0.1%

Length

2023-03-22T15:15:47.764591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:47.892503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5506
58.5%
1 2984
31.7%
8 910
 
9.7%
9 12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 5506
58.5%
1 2984
31.7%
8 910
 
9.7%
9 12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5506
58.5%
1 2984
31.7%
8 910
 
9.7%
9 12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5506
58.5%
1 2984
31.7%
8 910
 
9.7%
9 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5506
58.5%
1 2984
31.7%
8 910
 
9.7%
9 12
 
0.1%

r7
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
1
6564 
0
1862 
8
910 
9
 
76

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6564
69.7%
0 1862
 
19.8%
8 910
 
9.7%
9 76
 
0.8%

Length

2023-03-22T15:15:48.008186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:48.152800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 6564
69.7%
0 1862
 
19.8%
8 910
 
9.7%
9 76
 
0.8%

Most occurring characters

ValueCountFrequency (%)
1 6564
69.7%
0 1862
 
19.8%
8 910
 
9.7%
9 76
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6564
69.7%
0 1862
 
19.8%
8 910
 
9.7%
9 76
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6564
69.7%
0 1862
 
19.8%
8 910
 
9.7%
9 76
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6564
69.7%
0 1862
 
19.8%
8 910
 
9.7%
9 76
 
0.8%

r8
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
0
5242 
1
3244 
8
910 
9
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 5242
55.7%
1 3244
34.5%
8 910
 
9.7%
9 16
 
0.2%

Length

2023-03-22T15:15:48.304407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:48.436043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5242
55.7%
1 3244
34.5%
8 910
 
9.7%
9 16
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 5242
55.7%
1 3244
34.5%
8 910
 
9.7%
9 16
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5242
55.7%
1 3244
34.5%
8 910
 
9.7%
9 16
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5242
55.7%
1 3244
34.5%
8 910
 
9.7%
9 16
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5242
55.7%
1 3244
34.5%
8 910
 
9.7%
9 16
 
0.2%

r9
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
0
5790 
1
2677 
8
910 
9
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9412
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 5790
61.5%
1 2677
28.4%
8 910
 
9.7%
9 35
 
0.4%

Length

2023-03-22T15:15:48.587638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:15:48.719313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5790
61.5%
1 2677
28.4%
8 910
 
9.7%
9 35
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 5790
61.5%
1 2677
28.4%
8 910
 
9.7%
9 35
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9412
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5790
61.5%
1 2677
28.4%
8 910
 
9.7%
9 35
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9412
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5790
61.5%
1 2677
28.4%
8 910
 
9.7%
9 35
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5790
61.5%
1 2677
28.4%
8 910
 
9.7%
9 35
 
0.4%

Interactions

2023-03-22T15:15:09.350873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:05.889008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:10.686186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:15.322107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:20.242952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:25.327578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:30.684256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:36.258361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:41.146289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:46.089077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:50.639908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:55.704370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:01.705332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:06.568326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:11.829404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:16.569992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:21.993135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:27.336655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:32.367033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:37.571658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:42.374401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:48.197499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:53.273394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:58.354556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:04.283646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:09.671858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:06.037613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:10.809855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:15.440789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:20.368616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:25.467204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:30.878742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:36.549580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:41.272950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:46.205762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:50.768565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:55.835023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:01.931722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:06.705958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:12.059936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:16.710584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:22.296962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:27.477245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:32.495691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:37.915739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:42.671203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:48.312863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:53.413978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:58.509252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:04.415995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:10.035608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:06.166280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:10.938826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:15.565457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:20.494287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:25.650714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:31.166971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:36.810879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:41.409610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:46.331429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:50.905200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:56.144194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:02.068359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:06.848577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:12.392045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:16.835556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:22.632265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:27.602244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:32.631327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:38.228906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:42.999227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:48.468045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:53.559073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:58.665427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:04.540990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:10.337970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:06.288964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:11.062495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:15.682145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:20.615510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:25.874118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:31.440241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:36.933551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:41.536247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-03-22T15:13:49.758267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:54.570405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:59.676751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:05.321664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:10.863816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:15.680357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:20.509100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:25.782873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:31.433643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:36.257200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:41.285147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:46.153925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:51.198074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:57.275536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:03.137523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:08.241602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:13.419320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:09.938184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:14.438475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:19.288504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:24.341213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:29.992109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:34.691543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:39.954476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:45.353042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:49.933796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:54.725986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:59.837339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:05.677714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:11.023391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:15.832741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:20.665312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:26.126521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:31.581222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:36.408768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:41.425729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:46.491991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:51.555486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:57.433898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:03.287370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:08.410493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:13.755813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:10.081801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:14.733700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:19.676466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:24.543673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:30.128744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:34.938884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:40.287590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:45.500648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:50.079408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:54.889547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:00.193372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:05.953969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:11.181538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:15.988325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:20.805905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:26.470190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:31.750680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:36.572330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:41.581942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:46.883790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:52.012290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:57.621403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:03.645876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:08.582450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:14.097637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:10.249354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:14.874305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:19.824074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:24.737156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:30.268370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:35.266009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:40.617702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:45.648254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:50.216078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:55.041144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:00.570367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:06.106560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:11.330626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:16.135930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:20.977744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:26.809935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:31.906267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:36.739883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:41.734802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:47.275848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:52.369183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:57.826163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:03.801975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:08.742322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:14.765000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:10.395962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:15.017922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:19.963701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:24.915678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:30.406003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:35.597151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:40.862047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:45.793863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:50.358661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:55.190742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:00.988244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:06.258155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:11.499041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:16.285532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:21.321419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:27.039847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:32.054867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:36.909456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:41.874489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:47.664924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:52.774642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:57.996578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:03.942024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:08.897797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:15.104872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:10.537583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:15.176496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:20.104325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:25.129107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:30.543634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:35.920270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:41.003671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:45.941471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:50.497289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:13:55.546790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:01.360252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:06.418728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:11.688531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:16.434140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:21.665083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:27.196060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:32.214442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:37.159760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:42.030703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:48.031948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:53.123693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:14:58.181017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:04.098237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-22T15:15:09.072292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-22T15:15:49.223965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ididadeq3q4q6q13q14q16q17qp1qp4qp7qp10qp13qp16qp19qp22qp25qp28qp31qp34qp37qp43qp46qp49regiaoq1q2q7q8q9q10q11q12q15q18q19q20q21qp2qp3qp5qp6qp8qp9qp11qp12qp14qp15qp17qp18qp20qp21qp23qp24qp26qp27qp29qp30qp32qp33qp35qp36qp38qp39qp40qp41qp42qp44qp45qp47qp48qp50qp51qp52qp53qp54qp55r1r2r3r4r5r6r7r8r9
id1.000-0.011-0.089-0.037-0.0380.1420.1560.0110.1170.0530.0540.0520.0570.0570.0570.0550.0510.0550.0540.0550.0580.0570.0540.0540.0530.9100.0750.1670.0750.0800.1010.0620.0630.1040.1410.0450.0710.0870.1160.0390.0560.0070.0520.0230.0380.0350.0380.0280.0390.0270.0450.0160.0470.0230.0440.0400.0450.0120.0460.0000.0430.0010.0340.0130.0430.0420.0150.0390.0000.0440.0000.0490.0000.0400.0450.0520.0520.0440.1160.0750.0840.0740.0710.0710.0750.0740.087
idade-0.0111.0000.0850.0840.067-0.254-0.1250.098-0.256-0.186-0.180-0.179-0.177-0.177-0.177-0.181-0.177-0.177-0.174-0.174-0.177-0.177-0.177-0.175-0.1730.0200.3170.2130.2400.2390.2710.2420.1990.2380.2250.1660.1660.1750.1650.1670.2280.0820.2060.0600.1770.0370.1750.1140.1530.1340.1870.1070.1740.1170.1680.1270.2000.1050.1940.1360.1800.1320.1480.1150.1780.1720.0160.1590.0120.2110.1200.1870.0550.1860.1930.1880.2280.2290.2790.1640.1690.1650.1660.1620.1610.1610.163
q3-0.0890.0851.0000.4810.116-0.031-0.0620.111-0.030-0.325-0.319-0.308-0.315-0.310-0.315-0.319-0.312-0.315-0.308-0.308-0.315-0.319-0.315-0.312-0.3120.0651.0000.6010.7120.7130.7130.7080.5780.7070.7080.5040.5010.5030.5070.4990.7070.1340.4560.1860.4200.1650.4260.1780.3350.1360.4480.1820.4460.1610.4530.1510.4090.1510.4360.1650.4360.1610.3290.1610.4220.4720.1180.3440.0900.4400.1460.3940.1560.4170.5450.5450.6680.5450.5310.3370.3220.3280.3230.3240.3250.3340.336
q4-0.0370.0840.4811.0000.1070.0640.0340.0940.059-0.330-0.324-0.313-0.320-0.315-0.320-0.324-0.317-0.320-0.313-0.313-0.320-0.324-0.320-0.317-0.3170.0591.0000.5860.7090.7100.7120.7070.5780.7070.7070.5010.5010.5040.5050.5000.7070.1350.4570.1870.4210.1660.4260.1790.3360.1360.4480.1830.4470.1610.4530.1520.4100.1520.4360.1660.4360.1610.3300.1610.4220.4720.1190.3450.0910.4400.1470.3950.1570.4170.5450.5450.6680.5450.5300.3300.3230.3240.3190.3240.3180.3290.331
q6-0.0380.0670.1160.1071.0000.0970.0820.1160.088-0.300-0.294-0.284-0.290-0.286-0.290-0.294-0.288-0.290-0.284-0.284-0.290-0.294-0.290-0.288-0.2880.0431.0000.5820.7180.7260.7200.7770.5810.7070.7070.5050.5050.5060.5020.4990.7070.1330.4560.1860.4200.1640.4250.1770.3350.1350.4480.1820.4460.1600.4530.1500.4090.1500.4360.1640.4360.1600.3290.1600.4220.4720.1170.3440.0890.4390.1450.3940.1550.4160.5450.5450.6680.5450.5310.3080.3060.3070.3070.3070.3080.3080.308
q130.142-0.254-0.0310.0640.0971.0000.4680.0510.727-0.309-0.303-0.293-0.299-0.295-0.299-0.303-0.297-0.299-0.293-0.293-0.299-0.303-0.299-0.297-0.2970.0421.0000.7070.7070.7070.7070.7070.7070.7070.7080.7070.7070.7070.7070.7070.7070.1660.4570.1880.4210.1670.4260.1800.4120.1380.4490.1840.4470.1620.4540.1530.4100.1530.4370.1670.4370.1620.4040.1620.4230.6680.1200.4230.0930.4400.1480.3950.1580.4170.6680.6680.6680.6680.5300.3740.3740.3740.3750.3750.3750.3750.374
q140.156-0.125-0.0620.0340.0820.4681.0000.0460.460-0.304-0.298-0.288-0.294-0.290-0.294-0.298-0.292-0.294-0.288-0.288-0.294-0.298-0.294-0.292-0.2920.0411.0000.7090.7070.7070.7080.7080.7110.7070.7070.7190.7090.7090.7070.7070.7070.1660.4570.1880.4210.1670.4260.1800.4120.1380.4490.1840.4470.1620.4540.1530.4100.1530.4370.1670.4370.1620.4040.1620.4230.6680.1200.4230.0930.4400.1480.3950.1580.4170.6680.6680.6680.6680.5300.3760.3820.3770.3740.3750.3750.3740.374
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qp360.0340.1480.3290.3300.3290.4040.4040.3300.4040.3850.4250.4470.4210.4460.4450.4230.4280.4520.4510.4630.5770.4660.4480.4570.4790.0340.5720.3300.4040.4040.4040.4040.3300.4040.4040.3300.3300.3300.3300.3830.4280.0440.5180.0690.5510.0860.5390.0610.4440.1070.5780.0480.5510.0220.5510.0170.5660.0410.5880.0000.6180.0001.0000.0730.6060.4450.0560.4660.0560.5730.0840.5860.0660.6480.3850.3540.4640.4610.3220.1860.1860.1860.1860.1860.1850.1860.186
qp380.0130.1150.1610.1610.1600.1620.1620.1610.1620.2520.2120.2810.3300.4700.5150.3300.3430.3620.4500.3600.3060.7070.4760.3810.3820.0190.2300.1620.1620.1620.1620.1620.1620.1620.1620.1620.1620.1620.1620.1890.1720.2360.0910.2490.0160.3280.0500.5320.0330.5510.0300.3530.0970.3340.1030.3530.0700.4630.0330.4280.0790.4000.0731.0000.0000.4080.4690.0860.5400.1170.4210.1760.4490.0320.2030.1930.1990.1760.1000.0690.0690.0690.0690.0700.0780.0690.069
qp390.0430.1780.4220.4220.4220.4230.4230.4220.4230.4690.5430.5360.5320.5580.5520.5470.5500.5660.5820.5760.5820.7070.5680.5850.5780.0410.5980.4230.4230.4230.4230.4230.4230.4230.4230.4220.4220.4220.4220.4630.4470.0730.5270.1110.5290.0920.5320.1260.5330.0510.5860.0910.5290.0480.5340.0830.5710.0400.5900.0670.6010.0820.6060.0001.0000.5830.0000.6120.0000.5850.0980.5870.1270.6140.4950.4630.4710.4830.3280.2320.2320.2320.2320.2320.2320.2320.232
qp400.0420.1720.4720.4720.4720.6680.6680.4720.6680.5140.5920.5960.6170.6770.6870.6470.6550.6690.7320.7270.6790.7600.8370.7790.7110.0370.9450.5450.6680.6680.6680.6680.5450.6680.6680.4720.4720.4720.4720.5110.7070.1970.5430.2550.5250.2900.5270.3660.4370.3850.5600.3280.5500.2820.5630.3760.5350.4600.5640.4260.5630.3710.4450.4080.5831.0000.7070.5770.5470.6180.4900.5830.4460.5640.6080.5970.7320.6010.5100.2940.2940.2940.2940.2940.2940.2940.294
qp410.0150.0160.1180.1190.1170.1200.1200.1190.1200.2620.1820.2540.2280.3400.3980.2890.2330.2960.4620.4120.3230.4110.5820.4130.3490.0270.1710.1200.1200.1200.1200.1200.1200.1200.1200.1200.1200.1200.1200.1680.1280.1820.0360.2570.0340.2550.0340.4760.0600.4600.0440.3980.0480.3050.0540.3920.0290.5460.0520.5190.0270.4060.0560.4690.0000.7071.0000.0000.6630.0650.5720.0690.5020.0450.1410.1430.1650.1480.0730.0500.0500.0500.0500.0510.0500.0500.049
qp420.0390.1590.3440.3450.3440.4230.4230.3450.4230.3750.4330.4280.4480.4480.4550.4340.4370.4620.4680.4720.4690.4980.5990.5680.4730.0310.5980.3450.4230.4230.4230.4230.3450.4230.4230.3450.3450.3450.3450.3920.4470.0640.5030.0770.4980.0990.5150.1040.4400.0500.5600.0750.5320.0450.5670.0540.5300.0950.5520.0530.5720.0460.4660.0860.6120.5770.0001.0000.0000.6780.0650.6300.0980.5890.3950.3770.4700.4010.3240.1870.1870.1870.1870.1880.1870.1870.187
qp440.0000.0120.0900.0910.0890.0930.0930.0910.0930.2290.1480.2120.2250.3090.4020.2480.2810.2980.4320.3320.2810.4070.7070.4130.3140.0250.1320.0920.0930.0930.0930.0930.0920.0930.0930.0920.0920.0920.0920.1140.0980.1820.0000.1820.0340.2760.0340.3690.0300.4550.0000.3010.0480.3490.0280.3730.0290.4550.0000.3630.0270.4030.0560.5400.0000.5470.6630.0001.0000.0000.6450.0000.4780.0000.1160.1100.1270.1070.0370.0220.0230.0220.0220.0260.0220.0220.021
qp450.0440.2110.4400.4400.4390.4400.4400.4400.4400.4680.5240.5240.5330.5490.5560.5380.5350.5570.5900.5780.5920.5940.7070.6210.6070.0370.6230.4400.4400.4400.4400.4400.4400.4400.4400.4400.4400.4400.4400.4750.4660.1040.4940.1040.5040.1240.4910.1270.5180.1060.5550.1310.5120.1110.5350.1030.5150.0860.6040.0810.6020.0670.5730.1170.5850.6180.0650.6780.0001.0000.0980.6550.0880.6050.4950.4780.4860.5050.3460.2450.2450.2450.2450.2450.2450.2450.245
qp470.0000.1200.1460.1470.1450.1480.1480.1470.1480.2520.2360.2200.2320.2880.3560.3530.3110.2700.3940.3240.3610.3740.6030.7070.3920.0160.2100.1480.1480.1480.1480.1480.1480.1480.1480.1470.1470.1470.1470.2080.1570.2460.0560.1280.0440.1510.0300.2920.1220.3330.0480.3980.0890.3040.0870.2850.0770.3930.0690.3180.0840.4720.0840.4210.0980.4900.5720.0650.6450.0981.0000.0000.5280.0870.1750.1690.2020.1650.0890.0610.0620.0610.0610.0620.0610.0610.061
qp480.0490.1870.3940.3950.3940.3950.3950.3950.3950.4480.5060.5210.5090.5490.5400.5320.5320.5500.5550.5510.5630.5830.5860.7070.5860.0460.5590.3950.3950.3950.3950.3950.3950.3950.3950.3950.3950.3950.3950.4530.4180.1060.5020.2020.5220.1650.4900.1960.5180.1370.5430.0880.5420.0440.5660.0820.5370.1230.5500.0800.5650.0580.5860.1760.5870.5830.0690.6300.0000.6550.0001.0000.1180.5990.4810.4290.4500.4590.3070.2170.2170.2180.2170.2180.2180.2180.217
qp500.0000.0550.1560.1570.1550.1580.1580.1570.1580.2110.2040.3020.2780.3360.4670.3770.2830.3370.4240.3440.3010.4000.4590.3720.7070.0230.2230.1570.1580.1580.1580.1580.1570.1580.1580.1570.1570.1570.1570.2110.1670.2080.0760.3150.0400.2560.0450.3640.0800.4680.0540.3930.0900.2470.1100.3860.0570.4370.0620.3610.0500.4030.0660.4490.1270.4460.5020.0980.4780.0880.5280.1181.0000.0000.1780.1880.2040.1790.1040.0730.0730.0730.0730.0730.0720.0720.073
qp510.0400.1860.4170.4170.4160.4170.4170.4170.4170.4650.5340.5480.5380.5600.5520.5220.5260.5510.5460.5760.6000.5670.5690.5900.7070.0380.5900.4170.4170.4170.4170.4170.4170.4170.4170.4170.4170.4170.4170.4620.4420.0800.5270.0800.5590.0700.5620.0690.5480.0000.5660.0730.5350.0260.5410.0260.6030.0360.5780.0000.6040.0580.6480.0320.6140.5640.0450.5890.0000.6050.0870.5990.0001.0000.4930.4530.4640.4880.3300.2330.2330.2340.2330.2340.2330.2340.233
qp520.0450.1930.5450.5450.5450.6680.6680.5450.6680.5720.6100.6210.6140.6230.6240.6170.6230.6240.6070.6120.6150.6110.5990.6040.6210.0330.9450.5450.6680.6680.6680.6680.5450.6680.6680.5450.5450.5450.5450.5720.7070.1550.5290.2070.5050.1970.4920.2150.4050.1720.5170.2110.5160.1940.5170.1910.4820.1800.4870.1990.5040.1930.3850.2030.4950.6080.1410.3950.1160.4950.1750.4810.1780.4931.0000.6540.7210.6060.5090.2940.2940.2940.2940.2940.2940.2940.294
qp530.0520.1880.5450.5450.5450.6680.6680.5450.6680.5670.5940.5900.5890.6060.5930.5930.5960.6010.5960.6010.6010.6110.5960.5980.6120.0350.9450.5450.6680.6680.6680.6680.5450.6680.6680.5450.5450.5450.5450.5700.7070.1540.5230.2010.4750.1810.4960.2070.3740.1640.4900.2090.4880.1850.4990.1760.4490.1820.4660.1930.4700.1790.3540.1930.4630.5970.1430.3770.1100.4780.1690.4290.1880.4530.6541.0000.7250.5910.5090.2940.2940.2940.2940.2940.2940.2940.294
qp540.0520.2280.6680.6680.6680.6680.6680.6680.6680.7020.7460.7410.7330.7330.7290.7240.7190.7490.7210.7350.7420.7450.7310.7480.7390.0410.9450.6680.6680.6680.6680.6680.6680.6680.6680.6680.6680.6680.6680.6910.7070.1850.5150.2030.4840.1770.4670.1950.4610.1540.5110.2150.4940.1910.5080.1760.4620.1660.4800.2010.4830.2100.4640.1990.4710.7320.1650.4700.1270.4860.2020.4500.2040.4640.7210.7251.0000.7280.5090.3600.3600.3600.3600.3600.3600.3600.360
qp550.0440.2290.5450.5450.5450.6680.6680.5450.6680.5610.6150.6180.6110.6180.6190.6120.5980.6130.5970.6100.6250.6130.6040.6040.6220.0340.9450.5450.6680.6680.6680.6680.5450.6680.6680.5450.5450.5450.5450.5590.7070.1540.5140.2180.4740.1780.4740.1940.3980.1520.5170.2000.5210.1800.5180.1750.4760.1770.4950.1960.4980.1860.4610.1760.4830.6010.1480.4010.1070.5050.1650.4590.1790.4880.6060.5910.7281.0000.5090.2940.2940.2940.2940.2940.2940.2940.294
r10.1160.2790.5310.5300.5310.5300.5300.5370.5300.5330.5110.5100.5090.5100.5100.5090.5090.5100.5100.5100.5100.5100.5110.5100.5100.1190.5290.5320.5310.5320.5340.5320.5370.5300.5300.5300.5300.5320.5300.5330.5300.1170.3600.1470.3360.1130.3340.1180.3250.0740.3530.1210.3460.1100.3520.0930.3200.0910.3430.1050.3400.1090.3220.1000.3280.5100.0730.3240.0370.3460.0890.3070.1040.3300.5090.5090.5090.5091.0001.0001.0001.0001.0001.0001.0001.0001.000
r20.0750.1640.3370.3300.3080.3740.3760.3120.3740.3070.2940.2940.2940.2940.2940.2940.2940.2940.2940.2940.2940.2940.2950.2940.2940.0690.5300.3100.3780.3770.3800.3770.3110.3750.3760.3080.3070.3080.3130.3070.3750.0660.2540.1030.2380.0780.2360.0820.1870.0500.2500.0850.2440.0760.2490.0640.2260.0630.2430.0730.2400.0760.1860.0690.2320.2940.0500.1870.0220.2450.0610.2170.0730.2330.2940.2940.3600.2941.0001.0000.6280.6150.6230.6450.6090.6610.632
r30.0840.1690.3220.3230.3060.3740.3820.3110.3740.3070.2940.2940.2940.2940.2940.2940.2940.2940.2940.2940.2940.2940.2950.2940.2940.0880.5300.3080.3760.3770.3780.3760.3110.3740.3750.3090.3060.3070.3090.3070.3750.0660.2540.1030.2380.0780.2360.0830.1870.0500.2500.0850.2450.0760.2490.0640.2260.0630.2430.0730.2400.0760.1860.0690.2320.2940.0500.1870.0230.2450.0620.2170.0730.2330.2940.2940.3600.2941.0000.6281.0000.6260.6020.6140.5940.6230.625
r40.0740.1650.3280.3240.3070.3740.3770.3120.3740.3070.2950.2940.2940.2940.2940.2940.2940.2940.2940.2940.2940.2940.2950.2940.2940.0710.5300.3080.3760.3770.3790.3760.3100.3750.3750.3060.3080.3080.3130.3070.3750.0660.2540.1030.2380.0780.2360.0820.1870.0510.2500.0850.2450.0770.2490.0650.2260.0630.2430.0730.2410.0760.1860.0690.2320.2940.0500.1870.0220.2450.0610.2180.0730.2340.2940.2940.3600.2941.0000.6150.6261.0000.6050.6260.5920.6290.618
r50.0710.1660.3230.3190.3070.3750.3740.3110.3740.3070.2940.2940.2940.2940.2940.2940.2940.2940.2940.2940.2940.2940.2940.2940.2940.0720.5300.3070.3760.3760.3770.3780.3100.3740.3750.3060.3070.3070.3090.3070.3750.0660.2550.1030.2380.0790.2360.0820.1870.0500.2500.0850.2440.0770.2480.0640.2260.0630.2420.0730.2400.0760.1860.0690.2320.2940.0500.1870.0220.2450.0610.2170.0730.2330.2940.2940.3600.2941.0000.6230.6020.6051.0000.6220.6620.6430.611
r60.0710.1620.3240.3240.3070.3750.3750.3120.3740.3070.2950.2940.2940.2940.2940.2940.2940.2940.2940.2940.2940.2940.2950.2940.2940.0720.5300.3100.3770.3790.3820.3780.3120.3750.3760.3060.3080.3100.3160.3070.3750.0660.2540.1030.2390.0790.2360.0830.1870.0510.2500.0850.2450.0760.2490.0640.2260.0630.2430.0730.2410.0760.1860.0700.2320.2940.0510.1880.0260.2450.0620.2180.0730.2340.2940.2940.3600.2941.0000.6450.6140.6260.6221.0000.6160.6900.654
r70.0750.1610.3250.3180.3080.3750.3750.3140.3750.3070.2950.2940.2940.2940.2950.2940.2940.2950.2940.2940.2940.2940.2950.2940.2940.0740.5300.3110.3790.3790.3820.3780.3130.3760.3750.3060.3080.3090.3140.3080.3750.0660.2550.1030.2370.0820.2360.0860.1870.0600.2500.0880.2440.0800.2480.0700.2260.0630.2420.0730.2410.0760.1850.0780.2320.2940.0500.1870.0220.2450.0610.2180.0720.2330.2940.2940.3600.2941.0000.6090.5940.5920.6620.6161.0000.6290.610
r80.0740.1610.3340.3290.3080.3750.3740.3130.3740.3070.2950.2940.2940.2940.2940.2940.2940.2940.2940.2940.2940.2940.2950.2940.2940.0710.5300.3110.3780.3780.3820.3780.3110.3750.3760.3060.3070.3100.3210.3070.3750.0660.2550.1030.2380.0780.2360.0820.1870.0500.2500.0850.2450.0760.2490.0640.2260.0630.2430.0730.2410.0760.1860.0690.2320.2940.0500.1870.0220.2450.0610.2180.0720.2340.2940.2940.3600.2941.0000.6610.6230.6290.6430.6900.6291.0000.647
r90.0870.1630.3360.3310.3080.3740.3740.3130.3740.3070.2950.2940.2940.2940.2940.2940.2940.2940.2940.2940.2940.2940.2950.2940.2940.0850.5300.3200.3800.3810.3860.3780.3110.3750.3760.3060.3080.3110.3200.3070.3750.0660.2540.1030.2380.0780.2360.0820.1870.0500.2500.0850.2440.0760.2490.0640.2260.0630.2420.0730.2400.0760.1860.0690.2320.2940.0490.1870.0210.2450.0610.2170.0730.2330.2940.2940.3600.2941.0000.6320.6250.6180.6110.6540.6100.6471.000

Missing values

2023-03-22T15:15:15.613908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-22T15:15:16.289756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idregiaoidadeq1q2q3q4q5q6q7q8q9q10q11q12q13q14q15q16q17q18q19q20q21qp1qp2qp3qp4qp5qp6qp7qp8qp9qp10qp11qp12qp13qp14qp15qp16qp17qp18qp19qp20qp21qp22qp23qp24qp25qp26qp27qp28qp29qp30qp31qp32qp33qp34qp35qp36qp37qp38qp39qp40qp41qp42qp43qp44qp45qp46qp47qp48qp49qp50qp51qp52qp53qp54qp55r1r2r3r4r5r6r7r8r9
05831351113212may2015311110161503412118888888888888888888888888888888888888888888888888888888000010100
15826372104312may201531111002810111128888888888888888888888888888888888888888888888888888888000010100
25830358113313may2015411110141100412198888888888888888888888888888888888888888888888888888888011110100
35833373109114may2015511110141013412998888888888888888888888888888888888888888888888888888888001010100
45836388103210jan196060001013901292198888888888888888888888888888888888888888888888888888888001100111
55840381113331aug201521111013813211198888888888888888888888888888888888888888888888888888888001111100
65828375114331aug20152111101491321110108888888888888888888888888888888888888888888888888888888011101011
75827370113203sep2015301110131401312198888888888888888888888888888888888888888888888888888888000110100
85832377113203sep201551111012514212298888888888888888888888888888888888888888888888888888888010010010
95839359113222feb2016610110161711511118888888888888888888888888888888888888888888888888888888001110100
idregiaoidadeq1q2q3q4q5q6q7q8q9q10q11q12q13q14q15q16q17q18q19q20q21qp1qp2qp3qp4qp5qp6qp7qp8qp9qp10qp11qp12qp13qp14qp15qp16qp17qp18qp19qp20qp21qp22qp23qp24qp25qp26qp27qp28qp29qp30qp31qp32qp33qp34qp35qp36qp37qp38qp39qp40qp41qp42qp43qp44qp45qp46qp47qp48qp49qp50qp51qp52qp53qp54qp55r1r2r3r4r5r6r7r8r9
94025866351114210oct2016211110141301411118888888888888888888888888888888888888888888888888888888001010100
94035853350114310oct2016211110141611312118888888888888888888888888888888888888888888888888888888011101011
94046875364114305aug2016611110161801511118888888888888888888888888888888888888888888888888888888000111100
94056870365113208aug2016211110151301412118888888888888888888888888888888888888888888888888888888001010100
94066866369114309aug2016311110141812212118888888888888888888888888888888888888888888888888888888010101011
94076874352113309aug2016311110171602711118888888888888888888888888888888888888888888888888888888001000000
94086864375113210aug2016201110141001211118888888888888888888888888888888888888888888888888888888001110100
94096869381113313aug2016711110131101012998888888888888888888888888888888888888888888888888888888001011010
94106871351113115aug2016211110161212512298888888888888888888888888888888888888888888888888888888001111111
94116867379111215aug2016211110161401511118888888888888888888888888888888888888888888888888888888000111110